Eye disease is a major health problem among the elderly people. Cataract and corneal arcus are the major abnormalities that exist in the anterior segment eye region of aged people. Hence, computer-aided diagnosis of anterior segment eye abnormalities will be helpful for mass screening and grading in ophthalmology. In this paper, we propose a multiclass computer-aided diagnosis (CAD) system using visible wavelength (VW) eye images to diagnose anterior segment eye abnormalities. In the proposed method, the input VW eye images are pre-processed for specular reflection removal and the iris circle region is segmented using a circular Hough Transform (CHT)-based approach. The first-order statistical features and wavelet-based features are extracted from the segmented iris circle and used for classification. The Support Vector Machine (SVM) by Sequential Minimal Optimization (SMO) algorithm was used for the classification. In experiments, we used 228 VW eye images that belong to three different classes of anterior segment eye abnormalities. The proposed method achieved a predictive accuracy of 96.96% with 97% sensitivity and 99% specificity. The experimental results show that the proposed method has significant potential for use in clinical applications.
Complications of diabetes will result in Diabetic Retinopathy. The patient will not be aware of the symptoms unless it's too late to carryout treatment. Thus early detection of diabetic retinopathy is very essential to prevent vision loss. Fundus examination is the major method of diagnosis to detect Microvasculature changes, but a visual functional test is an effective alternative to this method which has potential to detect Diabetic retinopathy in early stages. Components of visual function can be characterized in by electrophysiological test of the of the retina.
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